Startup Growth and Venture Returns
Abraham Othman Ph.D. — Head of Data Science, AngelList
December 2019
We use AngelList data from thousands of early-stage venture investments to solve for how each successive
year of a startup’s existence affects investment returns. This allows us to create a model of how quickly
winning venture investments grow and how that rate of growth decays. Our model shows that at the seed
stage investors would increase their expected return by broadly indexing into every credible deal, a finding
that does not hold at later stages. Our results also suggest that startups staying private longer have created
a powerful engine for unbounded wealth creation entirely outside the public markets; we conclude with an
argument rooted in social equity for why retail investors should have access to a broad-based index of early-
stage venture investments.
Introduction
Venture capital has largely resisted the quantification that has revolutionized modern finance. In lieu of
mathematical modeling, venture capitalists tend to subscribe to pieces of folk wisdom around their investing
activities. How, when, and in what a VC invests all tend to have only the thinnest veneer of theoretical or
empirical justification.
In this paper we use AngelList’s broad database of early-stage investments to create and fit amodel of venture
capital investment returns from first principles. Fundamentally, we rely on two concepts. The first is that
startups tend to grow faster in their earliest years, for which we are able to provide empirical support from
AngelList data. The second is that early-stage investments have longer durations than later-stage investments
in those same companies, which is tautological. Taken together, the two concepts imply that winning early-
stage investments have more years to compound at higher rates of growth.
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Othman - Startup Growth and Venture Returns
While our high-level model is relatively uncontroversial, there are a number of provocative conclusions that
arise from fitting it to the Ange